{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "76520113",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "824f49bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Model    Dataset  Accuracy  F1 Score    TP   FN   FP    TN\n",
      "0      Basic Model No FFT      Basic    0.9833    0.9539  4815   67   32  1024\n",
      "1      Basic Model No FFT  CDD-based    0.9248    0.8739  4168  357  115  1635\n",
      "2      Basic Model No FFT   External    0.8287    0.7237  3347  840  109  1243\n",
      "3  CDD-based Model No FFT      Basic    0.9828    0.9515  4835   47   55  1001\n",
      "4  CDD-based Model No FFT  CDD-based    0.9388    0.8923  4300  225  159  1591\n"
     ]
    }
   ],
   "source": [
    "# Load the table from a CSV file\n",
    "df = pd.read_csv(\"inference_results.csv\")\n",
    "\n",
    "# Define a renaming map\n",
    "model_rename_map = {\n",
    "    \"Basic FNN No FFT\": \"Basic Model No FFT\",\n",
    "    \"Basic FNN With FFT\": \"Basic Model With FFT\",\n",
    "    \"CDD-based FNN No FFT\": \"CDD-based Model No FFT\",\n",
    "    \"CDD-based FNN With FFT\": \"CDD-based Model With FFT\"\n",
    "}\n",
    "\n",
    "# Apply the renaming\n",
    "df[\"Model\"] = df[\"Model\"].replace(model_rename_map)\n",
    "\n",
    "# Show the first few rows\n",
    "print(df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3aab743e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute standard metrics\n",
    "df[\"Sensitivity\"] = df[\"TP\"] / (df[\"TP\"] + df[\"FN\"])  # aka Recall or TPR\n",
    "df[\"Specificity\"] = df[\"TN\"] / (df[\"TN\"] + df[\"FP\"])  # aka TNR\n",
    "df[\"Precision\"] = df[\"TP\"] / (df[\"TP\"] + df[\"FP\"])\n",
    "df[\"FPR\"] = df[\"FP\"] / (df[\"FP\"] + df[\"TN\"])\n",
    "\n",
    "# Compute MCC (Matthews Correlation Coefficient)\n",
    "df[\"MCC\"] = (\n",
    "    (df[\"TP\"] * df[\"TN\"] - df[\"FP\"] * df[\"FN\"]) /\n",
    "    np.sqrt((df[\"TP\"] + df[\"FP\"]) * (df[\"TP\"] + df[\"FN\"]) * (df[\"TN\"] + df[\"FP\"]) * (df[\"TN\"] + df[\"FN\"]))\n",
    ")\n",
    "\n",
    "df.to_csv(\"results_metrics.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "90371b62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by Model and Dataset, then compute mean and std\n",
    "summary = df.groupby([\"Model\", \"Dataset\"]).agg({\n",
    "    \"Accuracy\": ['mean', 'std'],\n",
    "    \"Sensitivity\": ['mean', 'std'],\n",
    "    \"Specificity\": ['mean', 'std'],\n",
    "    \"Precision\": ['mean', 'std'],\n",
    "    \"FPR\": ['mean', 'std'],\n",
    "    \"MCC\": ['mean', 'std'],\n",
    "    \"F1 Score\": ['mean', 'std'],\n",
    "    \"TP\": ['mean', 'std'],\n",
    "    \"FN\": ['mean', 'std'],\n",
    "    \"FP\": ['mean', 'std'],\n",
    "    \"TN\": ['mean', 'std'],\n",
    "}).reset_index()\n",
    "\n",
    "# Flatten the MultiIndex columns\n",
    "summary.columns = ['_'.join(col).strip('_') for col in summary.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d48e6012",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save summary results to a CSV file\n",
    "summary.to_csv(\"model_performance_summary.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6af39769",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Model    Dataset  Accuracy_mean  Accuracy_std  \\\n",
      "0    Basic Model No FFT      Basic        0.98099      0.001614   \n",
      "1    Basic Model No FFT  CDD-based        0.92347      0.006067   \n",
      "2    Basic Model No FFT   External        0.82237      0.004505   \n",
      "3  Basic Model With FFT      Basic        0.97994      0.000965   \n",
      "4  Basic Model With FFT  CDD-based        0.89415      0.022359   \n",
      "\n",
      "   Sensitivity_mean  Sensitivity_std  Specificity_mean  Specificity_std  \\\n",
      "0          0.984123         0.002042          0.966178         0.003812   \n",
      "1          0.915305         0.009342          0.944115         0.005423   \n",
      "2          0.789109         0.007956          0.925370         0.008284   \n",
      "3          0.979203         0.001624          0.983414         0.005234   \n",
      "4          0.863786         0.032623          0.971848         0.003624   \n",
      "\n",
      "   Precision_mean  Precision_std  ...  F1 Score_mean  F1 Score_std  TP_mean  \\\n",
      "0        0.992774       0.000809  ...        0.94670      0.004600   4822.2   \n",
      "1        0.976614       0.001725  ...        0.87446      0.008013   4123.6   \n",
      "2        0.970396       0.002960  ...        0.71778      0.004182   3304.0   \n",
      "3        0.996355       0.001160  ...        0.94563      0.002346   4783.5   \n",
      "4        0.987496       0.001213  ...        0.83832      0.026341   3900.6   \n",
      "\n",
      "       TP_std  FN_mean      FN_std  FP_mean     FP_std  TN_mean     TN_std  \n",
      "0   27.332520     77.8   10.031063     35.1   3.956710   1002.9  26.568360  \n",
      "1   63.389799    381.4   40.527631     98.8   8.283853   1671.2  38.380551  \n",
      "2   33.313327    883.0   33.313327    100.9  11.199702   1251.1  11.199702  \n",
      "3   17.771700    101.6    7.988881     17.5   5.582711   1035.4  15.152558  \n",
      "4  160.823022    614.7  145.295140     49.5   6.132790   1710.2  43.057584  \n",
      "\n",
      "[5 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "print(summary.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "29f6fdcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x1200 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion matrices in pretty for each model and dataset\n",
    "df = pd.read_csv(\"results_metrics.csv\")\n",
    "\n",
    "# Define the display order\n",
    "dataset_order = [\"Basic\", \"CDD-based\", \"External\"]\n",
    "model_order = [\n",
    "    \"Basic Model No FFT\",\n",
    "    \"Basic Model With FFT\",\n",
    "    \"CDD-based Model No FFT\",\n",
    "    \"CDD-based Model With FFT\"\n",
    "]\n",
    "\n",
    "# Compute mean confusion matrices per model/dataset\n",
    "grouped = df.groupby([\"Model\", \"Dataset\"])[[\"TP\", \"FN\", \"FP\", \"TN\"]].mean().reset_index()\n",
    "\n",
    "# Set up grid\n",
    "fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(9, 12), sharex=True, sharey=True)\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.3)\n",
    "\n",
    "# Plot heatmaps\n",
    "for i, model in enumerate(model_order):\n",
    "    for j, dataset in enumerate(dataset_order):\n",
    "        ax = axes[i, j]\n",
    "        row = grouped[(grouped[\"Model\"] == model) & (grouped[\"Dataset\"] == dataset)]\n",
    "        \n",
    "        if not row.empty:\n",
    "            tp, fn, fp, tn = row.iloc[0][[\"TP\", \"FN\", \"FP\", \"TN\"]]\n",
    "            cm = [[tp, fn], [fp, tn]]\n",
    "            \n",
    "            sns.heatmap(cm, annot=True, fmt=\".0f\", cmap=\"Blues\", cbar=False,\n",
    "                        xticklabels=[\"Pred.: Pos\", \"Pred.: Neg\"],\n",
    "                        yticklabels=[\"Actual: Pos\", \"Actual: Neg\"],\n",
    "                        annot_kws={\"size\": 14}, ax=ax)\n",
    "\n",
    "        if i == 0:\n",
    "            ax.set_title(dataset, fontsize=12, fontweight='bold')\n",
    "        if j == 0:\n",
    "            ax.set_ylabel(model, fontsize=10, fontweight='bold')\n",
    "        else:\n",
    "            ax.set_ylabel(\"\")\n",
    "\n",
    "        ax.set_xlabel(\"\")\n",
    "\n",
    "# Add global axis labels\n",
    "fig.text(0.5, 0.94, \"Datasets\", ha='center', va='center', fontsize=14)\n",
    "fig.text(0.06, 0.5, \"Models\", ha='center', va='center', rotation='vertical', fontsize=14)\n",
    "\n",
    "plt.tight_layout(rect=[0.08, 0.03, 0.97, 0.92])  # leave room for outer labels\n",
    "plt.savefig(\"confusion_matrices.svg\", format='svg')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "10ed1531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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66+yqq67Kt5oRW7ZscVkOCloccsgh2XwHkJ/IbAAAAACAJFC9enVbv3697d69O7BszZo1Vrx4cTesISvvvfeenXfeeVne9uqrr9qFF15oV199tY0aNSrfakbIL7/84oZkfPPNN3bYYYcd8LmR/wg2AAAAAEASaNCggaWmptq8efMCy+bOnWtNmjRxdRhCKTCxfPlya9myZabbPvroI7v88svt2muvtccffzxfa0bI8ccfb++8844deuih2Xpu5D+GUQAAAABAEtBQie7du1ufPn1s4sSJ9vvvv7taC7ruZTmULVvWZTrIokWL3NCL2rVrBz2OMiM0hEHTXirbQPfzD38IV7chr2pGID6R2QAAAAAASWLkyJF2wgknWNu2ba1fv36uQGOnTp3cbZod4rXXXgus++eff7rhFZoFwk8FHn/77TeX3aD7+C+ff/55vtaMQPwiswEAAAAAkoRO6p9//nl3yWrIgp8KP+oSqlmzZpnWjbRmRJEiRbJdM4JpLQsmMhsAAAAAAAWqZgTiH5kNAAAAAIACUzMCBQOZDQAAAACAAlMzAgUDmQ0AAAAAgAJTMyKrGSsQf8hsAAAAAAAAUUWwAQAAAAAARBXDKAAAAAAAzurVq90lHNVV0AU4EIINAAAAAABn/PjxrmhjOHfffbcNGTIkX7cJBRPBBgAAAACA07t3b+vYsaNt377dWrVq5ZbNnTs3MB0lWQ3ILoINAAAAAJCg9mZkWOEIpo70hkls3bo1sKxBgwZWsmTJfHl+JA6CDQAAAACQoHSi/8HP6+2v7ekR3W/Htm2B61MXrbG0EiUifu7yxVPt9LoVI74fEgPBBgAAAABIYAo0rN+WvWDD5vV/2ub1ay19547AsoULv7PUYmnuetmKla1sxSp5tq0UqEwcBBsAAAAAAM7caZPs3QmPBi0b2btz4PrZPftb+1435tnzU6AycRBsAAAAAAA4rc7vavVbnxb2dmU25CUKVCZxsGHv3r02ZswYmzx5sv3999/WpEkTu+uuu+yQQw7JtO7jjz/u1s1Kp06d7MEHH8zZVgMAAAAAok5DJKI1TKJEamF3/li4cOGYFahEAQo2jB071iZNmmTDhg2zqlWr2iOPPGJXXXWVvf3221a0aNGgda+88kq7+OKLg5ZNnDjRXnnlFevRo0futx4AAAAAEJeKphR2gYbxL/zHVq/ZlK37/L1lo/3z91+Wnr4zsOy6AY9bamoxd71U6fJWukyFbD1W/aNr2AUdGuVw65GvwYZdu3bZs88+a7fccou1adPGLRs1apS1bt3aZs2aZR06dAhaX9EnfwRqyZIl9sILL9i9995r9erVy/XGAwAAAADimwINK1ZtzNa63375pi386q2gZRPHDQ5cP/7Ec61h0/Oy9VjVqpSNcEsRs2DDDz/84NJZmjdvHlhWpkwZO/roo23+/PmZgg2h7rnnHmvcuLGdf/75Od9iAAAAAEBCqndsG6tZu0HY24uXLJev24N8CjasWbMmy6IclStXDtwWzscff2zffvutvfnmm5ZbGRkZts0372s0FCpUKFB0BMlJRWi0b+U39j2w7yGR9r3/a+8+oKQo1oeNF5IRFDGBYAQVc84JxZxzQPQaMYs5Ys6CCXPOWTFj1mu+YMQAZgyoCIqCimS+89T/qzm9s7O7s8vABp7fOXN2d6anu2e2q7vqrbeqW9XgPuyFeK3XzOD5VrXFY694reZsGx+q/9f6JtXdUeTPzdC8efMwduzYSt/LXA0bb7xxWGaZZcKMmjx5chg2bFgoJQohGRqafQ0fPjx3jM9KHnvy2FNDOvZWW221kqzHa71mBs+3qi0ee5odr/XVCja0aNEiN3dD+h0TJ06sNGL2888/h0GDBoWbbroplELTpk1Dly5dQqmjfpq9Lb744rUWcdbszWNPs9uxVwyv9ZoZPN+qtnjsaXa81lcr2JCGT4waNSosssgiuef5u7IJH1966aXQrl27sN5664VSFZpSpWlKSX1LMVPD4bGn2lKXjz2v9Zrdjnk1bB57mh2PveJveBpC6Nq1a2jdunXMUkjGjRsX7zKxxhprVPi+9957L6y55pqhSZNq32lTkiRJkiTVM9Vq/TNXQ8+ePUO/fv1ipkLHjh1D3759Q/v27cPmm28epk6dGsaMGRPatGlTZpgFwYhddtllZuy/JEmSJEmqY6qV2YCjjz467LrrrqFPnz5hr732Co0bNw633nprHFv5yy+/hPXXXz8MHDiwzHtGjx4d2rZ1RlFJkiRJkmYH1R7XQHDhxBNPjI98nTp1Cl988UW554cMGVLzPZQkSZIkSQ07s0GSJEmSJKkyBhskSZIkSVJJGWyQJEmSJEklZbBBkiRJkiSVlMEGSZIkSZJUUgYbJEmSJElSSRlskCRJkiRJJWWwQZIkSZIklZTBBkmSJEmSVFIGGyRJkiRJUkkZbJAkSZIkSSVlsEGSJEmSJJWUwQZJkiRJklRSBhskSZIkSVJJGWyQJEmSJEklZbBBkiRJkiSVlMEGSZIkSZJUUgYbJEmSJElSSRlskCRJkiRJJWWwQZIkSZIklZTBBkmSJEmSVFIGGyRJkiRJUkkZbJAkSZIkSSVlsEGSJEmSJJWUwQZJkiRJklRSBhskSZIkSVJJGWyQJEmSJEklZbBBkiRJkiSVlMEGSZIkSZJUUgYbJEmSJElSSRlskCRJkiRJJWWwQZIkSZIklZTBBkmSJEmSVFIGGyRJkiRJUkkZbJAkSZIkSSVlsEGSJEmSJJWUwQZJkiRJklRSBhskSZIkSVJJGWyQJEmSJEklZbBBkiRJkiSVlMEGSZIkSZJUUgYbJEmSJElSSRlskCRJkiRJtRtsmDZtWujfv3/YYIMNwsorrxwOPvjg8OOPP1a4/OTJk8Nll12WW75nz55h2LBhM7rfkiRJkiSpoQQbrrvuunDfffeF8847LzzwwAMx+HDQQQeFSZMmFVz+7LPPDgMGDAgXXnhhePTRR0O7du1igOKvv/4qxf5LkiRJkqT6HGwgoHDbbbeFo48+OnTr1i107do1XHHFFWHkyJHhhRdeKLc8GQ8EGC644IKY2dC5c+dw/vnnh2bNmoVPP/20lJ9DkiRJkiTVx2DD559/Hv7555+wzjrr5J6ba665wrLLLhvefffdcsu/9dZboU2bNmHDDTcss/wrr7xSZh2SJEmSJKnhaDR9+vTpxS5M9sJRRx0VhgwZElq0aJF7vnfv3mHChAnhxhtvLLP8RRddFIMQvXr1CjfddFP49ddfY2DilFNOiVkONfHBBx8Edrlp06ah1Bo1ahT+mTQ1TC3+KymZpnM0Ci2bNg5/jB0fpkyZNku33aJ5k9CmdYswZvzfYfLUWbvtuP2mTcPcLVqGkX+ODpOmTp7l22/WuGlo33b+eFzVFo89j73Z7dirzeMOHnsz79hr3rx5WHrppWdoHV7rZ47Z+bifnc+38Njz2PPYm/XHXguv9aFJdVb677//xp8Mg8jf2NixY8st//fff4fvv/8+zvNw0kknxayG66+/PvTo0SMMHDgwzDvvvKEmhSX7s9TmbNY41KZ55m5Va9tu16p1qE0Uhto0s46pYnns1R6Pvcaz5XEHj73aPfYq4rV+5pqdj/vaPuY99jz2aovHXu0de+1n42t9tYINKZuBuRuymQ0TJ04MLVu2LL/yJk1iwIF5HVImA79vtNFG4bHHHosTS1bXKqusUu33SJKk+sNrvSRJs9mcDR06dIg/R40aVeZ5/l5wwQXLLd++ffsYcMgOmSBIsfDCC4cRI0bUfK8lSZIkSVLDCDZw94nWrVuHQYMG5Z4bN25cGDp0aFhjjTXKLc9zU6ZMCZ988knuOeZ24C4Viy666IzuuyRJkiRJqoOqNYyCuRp69uwZ+vXrF9q1axc6duwY+vbtGzMYNt988zB16tQwZsyYeAcKMhhWX331sO6664aTTz45nHvuuaFt27ahf//+oXHjxmGHHXaYeZ9KkiRJkiTVj7tRgIDC5ZdfHgYMGBCzFMheOPPMM0OnTp3i0Iju3bvHu1DsvPPOcXnmbCA48dxzz8XlV1111XDaaaeFLl26zKzPJEmSJEmS6lOwQZIkSZIkqWRzNkiSJEmSJFXFYIMkSZIkSSopgw2SJEmSJKmkDDZIkiRJkqSSMtggSZIkSZJKymCDJEmSJEkqqSalXZ1qwyabbBJ++umn3N9NmzYNHTt2DLvttls46KCDSrKNq6++Ojz22GPhlVdeKcn6VDumTJkS7r333vDEE0+E4cOHh+bNm4dll1029OrVK6y99toVHk/zzTdf2GijjULv3r1Du3btcq9VZ9lCeP9OO+0UjjrqqFCXnHLKKfFz3X333bW9K7OdffbZJwwePLjC1995550qj6v3338/cFfn1VdfPdQmz5sqJa/1qg6v98Xxel97vN7PHgw2NBAHHHBAfGDChAnh448/Dn369AktW7YMe++9d0nWX4r1qPZMnDgx7L///uGXX34JRx99dFhllVXisfLoo4/G5y+99NKw3XbbFTyevvzyy9C3b9/Qs2fP8OCDD4Y2bdrk1ludZaVibLXVVuH0008v+No888xT5ft79OgRLrroolqvfEil5rVexfB6r/rC633DZ7ChgWjVqlWYf/75c38vvPDCYdCgQfHCUoqKw5xzzhkfqr+uuuqq8MUXX4Snn346dOjQIfc8J/m///47nH/++bHnoaLjaZlllgnbbLNNuOWWW8Kxxx6be606y0rFaNGiRZljStL/8VqvYni9V33h9b7hc86GBl6Ak7Fjx8bejw022CAst9xyYZ111ol///vvv7llbr311rDpppuG5ZdfPl6Err322pialNKD0oUJv/32WzjppJPCWmutFVZbbbVwyCGHhO+//34Wf0IVa/LkybEyuvPOO5epeCTHHHNMuPnmm8scM/kWWmihsNlmm4Vnnnmmyu1VZ9nRo0fHFOAVVlghHmOkfWY9/PDDsQdmxRVXDCuvvHKMYn/yySe51+nZ4zl6btZYY42Yovnzzz/nXv/1119jBYioN8froYceGr777rvc6xzj1113Xdhwww3j+k899dTYK6S6aciQITEV+Lbbbss9d/nll8fz0I8//hiWXnrp+Bz/R9JjizkGWI7eP3rsVl111VgWOOftt99+4aabborHBscnvXfffPNN7n306nHu47jjvNm9e/cy+yXNCl7rleX13ut9Q+H1vmEw2NBAcUImos1YzlS4hg4dGq655prw/PPPx4L5+OOPx7Q3MMboxhtvDOecc0544YUXwgknnBCuv/768OSTTxYcB0gh/frrr+NJ+6GHHgrTpk2LF5CpU6fO8s+qqnFS/vPPP+OJtZAFF1wwXtwbN25c6XqWWmqpuK5//vmnym0WuyzHDxcFjjXSOy+44ILw4osvxtf4ee6558Zj69lnnw133HFHrBhQeQbHWzr5835ep+Jx2mmnxdfHjx8fxwTinnvuiWMyScvbfffd4wUJXFzokaFCPWDAgDDXXHOFgQMHVvn5VDtWWmml+D/v379/+OGHH8J7770XKwucu+hle/PNN+NyHAP04hVzDIDz4rrrrhsr6dtuu218jnUzHpRj5L777gu///573A5ovHEebNu2bXjggQfi+XbLLbcMl1xySRg2bFitfDea/XitVz6v917vGwqv9w2DwygaCCoPKcJGVJsHhTSNyVtvvfXiCTpFATt16hQLIpE6UIibNWsWJ5siSs1jgQUWiD8LTdhCet5zzz0XFl988fgcKXmc+OlVqWoyF816/F8w99xzz9B6uDCDNMyqUm2LXZYeNiLP4Hj66KOP4rFMTwkndioj22+/fXyd43PXXXeNFZK07j/++CMeq7zGxefKK6+MFwnQ0zJu3Lg4prRJk/873bE+0o6p9Bx55JHxYrTvvvvmLjhUznldteepp56KlYFCxwr/yyOOOCK8/vrrsRI6YsSIsOOOO+b+fykdk7HDPOgpq+wYSJOVUTbyJ9mjscXY5lRu9txzz7ieVPnguCF1PR3f9JZQkeX8SGqxVGpe61UVr/de7+sTr/cNn8GGBoJCkaJ5FBjSHK+44opYMCh8pJ3Ro8FMqaQT0VNBoV1iiSXiezi5E+HbYostQpcuXWLEj98LVUCotFAYU+UjRcpPPvnkWfiJVR2pUkhvx4z466+/4s/WrVtXa9kbbrghVpITKsapAkE6XBYV59deey3+TqWZNDbSfL/99tt4XHNip3cte8E477zzYuSbGbaZGZsJh0APHxUv1pNFbwnrpeJCWicpc1mkV2bT5zRrkV5Lj2s+xguDSgSVAM5b8847bzjjjDMqXFdVx0Cy6KKLlnsvM61nK+xUZmjcpTLFeZUeDrZBI+7zzz+Pr6XjUyo1r/Wqitd7r/f1idf7hs9gQwNBAckWns6dO8fnKBxvv/12HBf31VdfxWjg1ltvHcdyZgssBYnbI3344YfhrbfeiqlJd911V4wCEg3OStFC1R/0AHAi/eCDD+L/Px8nYaK/RPkr89lnn4XFFlusqAnEsstSQU4VgvzKyxxzlB3NxYmbnrcU8SYtmMoKKaGshwpwqriAixTHORUWeuKoiBBtJnWYdVFRJk240IWsUaNG8fc0XjnxGK9dHDOFKgNZHAf8f6k8UiFlDG8hVR0DSaHxy+k4LITt7rHHHvHcSWVp/fXXj5VYKr/SzOK1XlXxeu/1vj7xet/wOWdDA5ZOqJ9++mlMQWJ2Yk7URAcXWWSRGJlLyzD+7f77749RZ1KDSDdiDGihsWz0hhA5zE4SNWbMmDgRCylxqnu4wJOOyBhFboWVj4s1kzCRmliRkSNHhpdffjmXrluZ/GVJj+Rikh5Ep7OVlCzGzC255JLxd8bOsd8XX3xx7LkjWs24UHDs0vtx1llnxfXttddesbeDz0Jliqgz40gZ00mEOm2bHrzLLrssvPvuu3EsHxNosc0syozqrlGjRsX/O+m4NKroaWWsZiFVHQM1RQ8HPYecNw8//PCYBpzSl/Mrs9LM5LVeWV7vvd43JF7v6z/DeQ0EBY/IWzrwqVxceOGFcWwbFQmifEy4Q1SOAkOaG8tPmjQpl2LERCdEGJm8h4sHBbPQfWuZ3ZqZWCnwTMrC/b0Z58S66UVR3cSJ+o033oi9Ar179449B+nkSa8Aqbgp8ps9nriXNpFkxkYy/pdJnbKqs2whjLPs2rVr6NatW3jppZfiJFF33nlnfI2KAb0zVFC4eJAezPhjcOxSeeD9bLdXr16xkkX6MD19pA0T4aYCQ6X6xBNPjD0sTHRGhZzvAAcffHA89lme451ePyZdy0/31KzD/zMdU/n433Le4dzGMc3xx23XqKCmHjCO45Q2S4OrqmOgJtq3bx/HcTKenWOFijD3+kY6r0ql5rVexfB67/W+vvB63/AZbGggmGAnTRrFCZjIMifSfv36xTGWFExu7UKKJROqcKLnNi+czEElhQsRhZJIOAWccZyFxlGxfpajoHFxITWNsXNEmJs2bTrLP7uKQ0WRCzfHCbP5Ev0llYzbCjFpUraymT2e+J9SCSAdk9l481Mqq7NsIQceeGB49dVX4+2M6GkhAk3PGUj/PfPMM+MtiEhxo5JCZZfbGtEzwz7zWXgPsw0zWzXjL2+//fZc6iafmfewHV6nksz+kn4MelBIvaOSzm3euGUcvSvDhw8v4bev6qCxxKMQ0r1JF2d2fY4JHhwnVCRIb+TcxrGXerxobFV1DNQEM1FTKebcysRlHLucR+nh49ik500qNa/1KobXe6/39YXX+4av0XTzPyRJkiRJUgk5Z4MkSZIkSSopgw2SJEmSJKmkDDZIkiRJkqSSMtggSZIkSZJKymCDJEmSJEkqKW99KWmm4/ZDkydPru3dkCSp3uJ2k40bN67t3ZCkohlskDTTcGfdkSNHxvu6S5KkGdO2bdvQvn370KhRo9reFUmqksEGSTNNCjQssMACoWXLlmGOOeao1cCHlTNJUn3ENWz8+PFh1KhR8e8OHTqEuqi2r7W1vX1JZRlskFTOPvvsEwYPHpz7mws3wYLFF1887LjjjqFHjx6hSZMmVQ6dSIGGOeecM/z000+hc+fOYVabNm1a+PXXX+P+0yMkSVJ9xHUMBBy4ts7okIpSXOuzvvrqq3DGGWeEBx54IMxqkyZNCv369QvLL7982H777Wf59iUVZrBBUkHLLrtsOOuss3KBg7Fjx4bXX389XHTRReG9994LV155ZaWZCmmOhlatWsX3/vvvv6E2TJkyJfz++++hY8eOtbJ9SZJKhWtqusaWYv6GGb3WZz333HPhww8/DLWBAMydd94Z91tS3WGwQVJBrVu3DiuvvHKZ5zbZZJOwxBJLhAsuuCA8/fTTRfUemM4oSVJplPqaWqprvSQV4q0vJVVLz549w4ILLphLk5wwYUK47LLLwuabbx7TF1ddddWw//77h2+++Sa+/ttvv4XRo0fH3z/99NM4pCFlHPz888/hiy++CJ999lkYNmxY+P7772MqZDJx4sT4HK+xDOv866+/yuwP22eZoUOHxkd2Hfz88ssv4+8M42BbkiSpNNd6rs+4+uqrwzXXXBN/X3rppePfGDNmTDjnnHPCxhtvHN+35pprhiOOOCKMGDEit60ffvghHHrooWGttdYKK620Uthjjz3Ca6+9VmZ/uJYfcsghcbs8WMePP/4YX2Nd3bt3j7+feuqpMVgiqW4w2CCpWkinXGeddcLHH38cAwYnnXRSePTRR0OvXr3CbbfdFi/0jNu85JJL4vJzzz13mGeeeeLv9JS0a9cuTuBEUODvv/+OlZlFF100zD///OGff/6JAQikZZhzoVOnTnEZxo7yHEEI8PPbb7+N+8EyDJUgwJCeY/lFFlkkLsv60+9SfcK4airve+65Z4XLHHvssXGZU045ZYa2NWjQoLgefs7M90hqGNf6448/Pl6vd9ttt7DrrrvG9z744IPxb54nQPDWW2+FE044Idx6663hyCOPDO+8805u6AbXeJZhqOWll14arrvuuji/0mGHHRav9xg+fHg8/zEkkroFGRcEGvbaa6/4HPNXpEAH70u/S6p9DqOQVG3zzTdfHC/KBJAECPr06RO23nrr+Bq9FgQR7rrrrjj+k/uCpwmmsmNNqchw+y4mj0ypnAQK/vjjj/g3lRv+phLRpk2b+BwTVzEukwoM+J31LLbYYrmxq6yPHhCyKZitu0WLFvH5Zs2a5SbXkuobjvOPPvoo3uGFcpPFDPWvvvpqre2bpNn3Wn/xxRfHDEbOS+nclIZlpMmZTz755LD66qvH58heIJOBgAQIFtBBcPjhh4eNNtooPrfiiivGgEHKUuR31nPHHXfEugIIhGy66abhlltuietfZpll4vN0KjAPhaS6wWCDpGpLjX3GjtJTkSoV9D589913uYZPWi4fAQhmu+Z1KhM8yFKg0ZTeQ4CiefPmcfgDFRoqGDyyt/ui8kNwgYZYeh9BB57jNamhoPL89ddfxwnY9ttvvzKvUd6oiM8111y1tn+SZt9rfXb4YxaZi3Q8sB6GOpCpQGDhgw8+yL2HgEaXLl3iXSzefPPNsP7664cNN9wwZk4k//vf/2Jwg84DOiJAfYAAxttvvz3TvwdJNWewQVK1Udngok+q4xtvvBEuvPDCWIGgkd+1a9dcBkNl6ClhPWlGbdaXnfGayg0ZC2QojBs3Li7Pc2Q5LLTQQjEYQaWDmbN55CvFLN1SXUGZotevULBh4MCBYYsttogzyCcE7+jxe+qpp2LAjiAdKc4HHXRQmXLGeOzbb789/PLLL7E3cZdddim3bYY2cUs5GgKsl15LehIr6j1kbDe9na+88kocr80QJ1KqDzzwwJJ+J5LqxrW+oo4FPPnkk+Hyyy+P5xjWQwZCyjgE13WGZVx//fXhxRdfDI8//njskCBrgbkeGIrJ9Z/zHI98DM2UVHcZbJBULTTwGZvNBE00YpikiUrBjTfeGBZeeOFYcbj33ntzE0QWQtYBvRzzzjtv7NWgYgFSxMluSHiewAINJRowBB0IPhBo4HkCCvRusJ583gVDDQ3py8ccc0yZoRRk/RBkIGCQgg1U/JlsjWEXjI+mUUCZ5RZ2jHM+77zz4nL33HNP/P0///lP7ElkHDW9i1kECxgrTeYEr/GT28vtvffe4ZFHHgmdO3cut580SAhMEJCgfLNfjMWmoVEomCGp/l7rCUJUhFtnch5g3hmCjWQ6gPPB+++/n1uO588+++w4j8Pnn38eg6o333xznO+J5+hkWHfddeOElPnSME1JdZMlVFK1MM6SBv+ZZ54Z7y5BTycTRmUnX8yvfOQ3/FNAgfkYUgYCDSQaTul3Josi5ZKJIek9oZHDg7tRkA0BelcIQvB82kZK13SOBjU03bp1i8d0NruBnkCCbauttlpuORr3pBbTm7jNNtvE59Zbb73Ym3jVVVeFfffdN6YtMxEbAYzTTjstLkP6MmUwzT4PAgv0Kt5///1xAlYQmOB9rKt///7l9nPw4MFxe2nbjNGmDBcKCkqq/9f6lNmQzZrChx9+GCeAPOqoo3JzLzGXUxr6wGtDhgyJgYwbbrghZleR+cCDu1GkCaMZQsEwMp5PwQW2yaST1BF43mxGqW4y2CCpIBod9IymCgETN9JbSQWEe25z+yuCAVz4+/btGw444IA4BnPAgAHhv//9b8xGyM6jABotKXAAKhL0XFD5oAeVwEHaXhpWQeCAoATbISOCZVKjhTtMkNLJfpBKScCB/SQDgp6X7Lb5PMwBUcwQD6kuokxwS7dssOGZZ54JW221VZmAHo19ysuWW25Z5v2UWwIEvE7ZYmI2bkeXxbqywQayHajI0/OYxkrzXgIOpEcXQnCBdZCBwdAPHjQmJDW8a322AyHNG/P000/HW1gSPMC5554bs5oY8kg2BNkL6X0Mx+Lcxt0uCEqQDUUwgltqEhgFk0eSYcVdK7gDBddy9u+ll17KBTxTMINzFhlXbF9S7TPYIKmgoUOHxntdg4YMWQRLLbVUTHVk/DXoUeC+28wUze2mGFvJeO677747VhzSBFBUQAg0kIpJcCENjWAGawIDVGJYPz0mzFJNBYSKA3M2MGaUsZ5UgshW4L3pVpoELbidJsuke3ZTCWE9qdJDsIHgBBUoKlXcoi+/90WqLwgGMDSChjzHOhVrhlZkUaGnjOT39BGcA9lBaZ6TVJbyl0kotzQ0lltuuYL7QwZSvtNPPz0O8yAYwTANHqussko8dzCkQ1LDudYzRILhElxbCUw88cQT8Ra8zBHDOsiMYJgXQVICCQQjWQ8BSIZSEIxkzgbWzy0tqRNw7SdAsfPOO8ftc94gSHHFFVfEugUdGezjtddeG7p37x6XYUglwywIQpAVwe020xBNSbWn0fTKZnWRpBoiA4EZq7nrRHYyKEnVQ2UeVOwJ4HHLN3oAydJhhvjnn38+vk7WA+nGBNe4RdzHH39cJuDAfA2MuaYSz3JkPnDP+h133DG3DJX/Hj16xBnkaRTQG8l2qOAXQq8kDQ16INN7ssheYsZ6hmwwZwOZGJJqzmurpPrE7j1JkuoJsnsIGBBgePbZZ3PzImQRSGDIAz2JWWnYA/M70HNIdlH+MulWdtl1pYbNCiuskHvQe8kEkfnZEzSEuDMGPZUgE4nJJNnPNP5akiTNHhxGIUlSPcLkjIxdZjhQnz59yr3OfApkGPAaQ4xIQWaeBmZ332mnneLkkGByteOPPz4uR5YD47aZCDKLuSEILPCTsdoMu+D2cw899FA49dRTy22bnlaGXJAmTQozqdUEKx577LEYhJAkSbMPgw2SJNUj3AKOOUnITCh060nGXXN7OiZOYzgFk6926tQpHHfccWVuHbftttvGgAVDHAgoMAaaIRYslzAxJJM9Mp6a8dfMSE9WBGOrGZNdCOvgNptkNzCbPcM6WLZ3794z6RuRJEl1kXM2SJopHFcqSVJpeW2VVJ84Z4MkSZIkSSopgw2SJEmSJKmkDDZIkiRJkqSSMtggSZIkSZJKymCDJEmSJEkqKYMNkiRJkiSppAw2SJIkSZKkkjLYIEmSJEmSSspggyRJkiRJKimDDZIkSZIkqaSalHZ1klS1adOnhzkaNao3295kk03CTz/9lPu7adOmoWPHjmG33XYLBx10UMn27eqrrw6PPfZYeOWVV6r93hEjRoTu3bvH3wcMGBCWW265cststdVW4dtvvw133XVXWGuttWq0j2k7xa6jquUHDRoU9t1337D//vuHU045pdzrSy+9dLjooovCzjvvXKP95bs49dRTC77Wtm3buP3sd1fIU089Fc4777wwePDgCpfheKjJ/602TJs2Lcwxxxz1ZrtTpkwJ9957b3jiiSfC8OHDQ/PmzcOyyy4bevXqFdZee+1Ky+l8880XNtpoo9C7d+/Qrl27Gi1bCO/faaedwlFHHRXqEsoQn+vuu+8u+Po+++wTj2N+9unTp9zrN910U7jsssviZ7v44otrvB+snzJR7DqqWp7vG08++WRo3bp1tT5zMTjPVOSGG24IG2+8ce67K+SAAw4ISy65ZIXnmmRGzr2SVB8ZbJA0y9HYf/Hr38If/06epdudp2XTsFmX+Wr0XiqTPDBhwoTw8ccfx8p6y5Ytw957712S/WP9M7ouGk3PP/98uWDD559/HhtqddWdd94ZNt9887DqqqvOlPW/+eab5Z7Lb/gS7FlllVXKLTfPPPPE1yZP/r/j9ZdffomBpuzyjRs3DvUFn/vK/z4fRowdM8u22WnuduGYbltU+30TJ06MgSi+86OPPjp+35S/Rx99ND5/6aWXhu22267Ccvrll1+Gvn37hp49e4YHH3wwtGnTpkbLNiScI1544YVw+umnh0Z5gdeBAweWe66uIKDA//vcc8+dKes/7bTTwtZbb13u+bnnnrtMwJbvLR/XgSZNmoQNNtgg9xyBqPbt25dZPrsuSZodGGyQVCsINPw2ftYGG2ZEq1atwvzzz5/7e+GFF4694jR6ShVsmHPOOeNjRqyzzjrhueeeC8cdd1y5RsTqq68e3n333VAX0atJryC91y1atCj5+rP/u4rQEKhoObIgsg3gqpav6wg0fPv76FDXXXXVVeGLL74ITz/9dOjQoUPueRpwf//9dzj//PNjr3cqN4XK6TLLLBO22WabcMstt4Rjjz0291p1lm1I6Fl/++23wwcffBBWW2213PMEI7/77ruCWVF1Af8fgkBbbrllWHfddUu+foJLVZVnzk2VLZM9dxHUqWp5SWronLNBkmoov1E8duzYmO1A7xYVdhr+/P3vv//mlrn11lvDpptuGpZffvnYSLr22mvD9OnT42v0lKd0Yfz222/hpJNOio0DGgWHHHJI+P777yvdJ3reWGbYsGFlnn/22WcL9to9/vjjYfvttw8rrrhi3PZ1110Xpk6dmnud3l6GOay88sphs802C++88065dRBwYbusg59kKZAyXx1nn312GDlyZLj88ssrXe7DDz+M+8P3wfdCgOKPP/6o1rZUP5BJwrHFEJpsoCE55phjws0331xlcGqhhRaKx+4zzzxT5Tars+zo0aPjMKoVVlghlh2GemQ9/PDDMeuCckH56dGjR/jkk09yr5MdxXNka6yxxhqxJ/znn3/Ovf7rr7/GgAdBQo71Qw89NAYDEs4blNcNN9wwrp+ykAJhlaHxyzoJSuYHJLt16xaDMFnffPNN3HY6D5Fhkh2CMmnSpHDhhRfG8x2vkx2SX/5Zx8EHHxw/6/rrrx+OP/74+P1VB+cptpECTRX5888/wznnnBOHxPDd77nnnjEwLEma9Qw2SFIN0FCgt5V0+uzY4aFDh4ZrrrkmDmWg8k9jnt44MKb/xhtvjBVh0phPOOGEcP3118dxyIXGqZPi/fXXX8cGxUMPPRQr8DRussGAQhkCVLCzDQn2ddy4cWG99dYrs+wdd9wRzjjjjLDHHnvEfWCsOsGQNG76r7/+Cvvtt1/s8aPhRECA/c3is5HafOSRR8YGWmoA9uvXr1rf52KLLRYbVoy7fu+99wouw+dg3DRjo/k+6PUeMmRIOPDAAyv9TlQ//fjjj7HhWNHQmgUXXDAe68UMYVlqqaXi+v7555+SLcsxSKOdssOQjgsuuCC8+OKL8TV+ku5PeSXQR1kjEJDmSeB4JXhIkIH38zqBBlL5MX78+His45577onlguE8u+++ewxCpPkVyMAgIMncJHPNNVcMGBSDoCDnoBToBPtJVkcWQQXOD82aNYtBxNtuuy0GCRhqkhr8ZJewXc4bDzzwQAwaZssw+0tQZdFFFw2PPPJInAOB97JePmexGN7Bd0xQ95JLLim4DN8r5022T9CD74X/J+cIzh+SpFnLYIMkFYEgAb1yPMhKIMjQqVOnMuPFacwzmeFKK60UX6MnjonsyA7ADz/8ECvtBAToQSXTgEYGDY58ZBCQPs5kbfQWdu7cOVbqyYqgsl1VQyIbbKARscUWW5RplNHIIChAo4FhIDT2d9hhh9href/998dAA8EDsjJoRNDA5/OlxlBCIOSwww6LjRTSnNkOQQMaSMX0smalDAq2kc0GSWjoMJEbARK+DyYHJBPis88+KzgnQ1b632Uf2V5kpJ7X7KNQIEizRjrOSzHOnYY4KusRr+6ylEV6/BdffPEYGKDccYymYTc0jClTlHeO61133TV3LmDdZOQssMAC8XUyoa688soYrANljwAhDeauXbvGBjPrY3JEghyUXwIQlJltt902LLHEEjG4yTCQYlBOCRqQKQT2i3kxyAbIuu+++2KmA8FD9oNzW//+/cPvv/8ehzzxOWjQE6jkvZwnyHJgss2E8wlzFxBoodxy/uSzso787Iqq8F2dfPLJ8TsoVOZ5jvMB580111wzdOnSJQZ32S8CqZU566yzypV/AiP5k8XmL1PKSYIlqaFxzgZJKgKpuKmnkawDhipcccUVsaFOrz9BBHrvyF7gjhKkO5OVwJ0OaAiA4ANp4VT0qQQz7pjfCTzko/JPI4uGTLYnl4p2VRjTTLYBk0LSOCfYkN8TOGbMmDhMIztmG1TQSV/nrhXsA0GI7ER52QkUWUca+kCWQUIGBoEGPjt3DqjOxIUEa2igsc78idjYn/zsDBpA7B+BmfyGUhYZJvlo6GURzKExlTXvvPMWvf8qrXRHCLIbZhTBM+TfyaCqZWlsEmhMCC6mCQrzyw7HzmuvvRZ/J4DI0AGGSVGWOF9wjKbhBZRtGqnc5YTGO4Ezjl8CFiBDimBLfiCScsV6CVQQLGAIRxZBDV6vCsc16yYDi8wRMhMYPsJ5LL/MERzIPs8wDM5LvMY8D5wvsvuR7haS8Fm++uqrcpOvps9SXWREsN8EL8guy99fzgcEZ7IZEWSgVBWQJNDKJLVZ+YEuhsuQkZY1M+aYkaSGwmCDJBWBSidpwAk9dDxHgIHJ1hg3TVo0lWp6GslaoLeSXvhs44neQHoT33rrrVj55VZojNVmGEIWM5vXFMELGh30GpKmTGoxDYtsT342fTorNYbYPpX0/LHX2f1Kr9GjWmjCNsbZjxo1qkbDKcimIBCTVdE+8zyTsVUm+7+rCMGcYpbTrEGmDD3kTGRYaL4RGqr09nP80XNdGXq7ObaKmYA1uyxBxhQAyA9W5N/NhPKQGuX0gDOsiuAEjXnWQ0M4eycFGq2cPwhQkMlE4IFhEQTGWBcN+vxhSyDTIN0xIr9MVOe8wXfK+tlPApKF7rJQ2XmCMlfMfrAswRQyB/LV9I4fBAb5bglOFrO/PF/Vd0MApqryzzHhOUKSiucwCkmqoVSxpTLNhIyvv/567OGnEUEWwyKLLBKHTqTlSMknpThNskYqMMMxCo2zJvOBns3shJBkEjBJ20cffVRUdgO9fzQi+D2/YUQjjsf7779f5nnGOtOIYN/JGiBDg+0mn376aZnKOQEUxrdTAU8PGmukSdfUf/7zn/gd5d+zniyN/P0le4NUboI/alg4Zhl6QJo+Kf75aJgz4SKp9ZUh++bll18uM+Sp2GUZDpE9trOZLhznWRybKejBfArsO0Ezsp8I9lFOwPmAbAca36xvr732itkNfB4CKBzT9MwTHKQxnrZNEJHhAdxRhvkbCObll4ds+awKmQxkRzDvCueaQgFDyhzfMZNAJmREcV6izBEQIZOBgFBC5hefIeE74XOxv+mzEKhluEUaVlJdfBcESZgDIjs/BPtLZkp2vXzffE+cUyVJs5aZDZJqxTwtm9arbZIhkGZPp/JKEIHKMqn4zJDO+Gp6zmjc0wAn9ZsUbN6TKuqkDTOcgd4x0npp2NBw4Pd8rJP0ZYZNMIcB93FnaATrLubWdPTG0tAhs6CiscpMmsZQEHqQGZ7ABGpMbkmaMo0c5mGg55OZ49kPPiM9yQm9msxzwDqo/JPdQao4E0l27969XEp2sVgv3y3DKbKYhI+eYHqA+Umjh99J2eb7UvE6zd2uXmyPORHeeOON+P9mXgCyBChbBO3IAODYy949IVtOJ0yYEI9HAl/MocLxk1WdZQthXgUCctzB4aWXXoqTQjKJImhY0wAnIEFZYngV85iA8wHBAt7Pdnv16hUDKwy/ohHOsCsa8QQsCEqeeOKJMaOC+VEIaPI9gLLH+YTlOYeQNUUZzh/eURHOJQQvmReCsl6o559ACN81+8DcLOw722T/eQ/nMuZ9IVjC8AoCEMxbkSaxBP87AhoEYQ8//PD4HOvg+84Od6guArVkb5Ehlu5Wwp0umLeCcxZZZQRz+N4JPhTKrJAkzVwGGyTNctOmTw+bdZmv1rY9x/9P/a0OKtBp8jcaBvR4UsFn4jQCATxo3HP7Sm6BR8WbRgh3c6ChkSrHNJRoNNBTS8OCoQL5Y4DTNliONGEaPjTASUWm97OqIQNpSAANMwIaDKkohFnb0yzzNO6ZxI0GDEEI0IjjNRr0NDrYXxo/2YwD1kHPJpPV8fnJlmDGfJabEfR+HnfccWWCG4yJ5/PTINxxxx1jA4xJ+mhYFPOdKOQycY7ptkWtbDc/w6YqlCsai5Q9JjSlt58x8gSYOObyA3XZcsoxQSOU4QIcp/lDKKqzbCGUk1dffTXOL0J2BVkHNN5BQ/fMM8+MDXHKGEEJgoUMESJTgP3m8/AeygtDnSint99+e26oBp+b96S7rRBkZH9TFg8ZE3ynBAQJvHHLXbIpmEehWAQlGdKVfxeKhMAL+0FAIt2VgsAkf6eJNCl/nAMYIsIdPFhn9ha+BDNZB5+V8wgT1XJuYghZmpejptJwioR18x0RzGBoGsERgrZMxFvReVCSNPM0ml7RADdJmgH02FHppYfOCbQkSZpxXlsl1SfO2SBJkiRJkkrKYIMkSZIkSSopgw2SJEmSJKmkDDZIkiRJkqSSMtggSZIkSZJKymCDJEmSJEkqKYMNkiRJkiSppAw2SJIkSZKkkjLYIEmSJEmSSspgg6RZbtq0afVq25tssklYeumlc4/ll18+bLHFFuGWW24p6b5dffXVcVs1MWLEiNz+ffbZZwWX2WqrreLrgwYNqvE+pu0Uu47Klh87dmxYZpllwn333Vfm+W+++Sa+p9B3sf/++4devXrF31lmwIAB8ffJkyeHO+64Y4a+S96T/T9nHzvvvHNchs9R0TI8/vnnn3LHS/5jn332CbVp6rSp9Wq7U6ZMCXfeeWf8H6yyyiph7bXXDgcccED43//+V2U57datWzjrrLPCmDFjarxsIbyf46WuOeWUUyo9vniNz3v++ecXfP2mm26Kr7OeGcF2qrOOypY/8sgjw2677Vbu+d133z3u6+DBg8s8/+STT4auXbuG33//vdz38f7774f33nuvRuey7Hsqenz55ZdxucrOAffcc0+l55r0YFuSVN81qe0dkDT7mWOOOcKNd70efhn55yzdbof2bcMh+25Yo/fSuOGBCRMmhI8//jj06dMntGzZMuy9994l2T/WP6Pratq0aXj++efDcsstV+b5zz//PAwfPjzUJXPPPXdYdtllw4cffhh69OiRe/6NN94IHTp0CD/99FP49ttvwxJLLJFrdH700UfhmGOOiX+/+eaboU2bNvH3p59+Olx00UVhv/32m6F9at++fXjkkUfKPd+kSdnL5cMPPxz3MV+rVq3i+6dO/b+GNZ/tqKOOKrM8/6Pa1HiOxqH3HWeFr0d+N8u22aX9YuGq/c6p9vsmTpwYA0y//PJLOProo2OwgfL36KOPxucvvfTSsN1221VYTmn89e3bN/Ts2TM8+OCDueOluss2JBx/L7zwQjj99NNDo0aNyrw2cODAcs/VtnXWWSeWbf5HLVq0iM/9+eef4ZNPPollivPFmmuumVueYALBhnnnnTd+xlQWwXmGda2++uoztE8ECzgW880zzzwFj6+s1q1bx6D3nnvumXtu1113DVtvvXWZ5du1azdD+yhJdYHBBkm1gkDD9yOq7kGsK2hEzj///Lm/F1544dgjRqOnVMGGOeecMz5mtGL+3HPPheOOO65cI4IK9rvvvhvqEvb32WefLfMcQYTtt98+BhBoSKRgw9ChQ8P48ePDeuutF//O/j+mT59ekv1p3LhxmfVWhIZARctlGwkEVKpavjYQaPj0xy9CXXfVVVeFL774Ih4L2eAOjci///479tDTi5zKTaFySvbMNttsEzORjj322Nxr1Vm2IVlrrbXC22+/HT744IOw2mqr5Z4nGPndd9+VC1TWNjJZyFwiuLDGGmvE59h/ggm77LJLePnll8Pxxx9fJthAlgpmVsCIcl1Vec4/vvJlz/Wcd6paXpLqI4dRSFINpV627LAAsh022GCDWGGnIc3f//77b26ZW2+9NWy66aYxbZtG0rXXXptrKOen/v/222/hpJNOio0DGgWHHHJI+P777yvdJ4ZKsMywYcPKPE+Dnp6zfI8//nhs2K+44opx29ddd12ZnkB6e/fdd9+w8sorh8022yy888475dZBwIXtsg5+kvJe7HAVviPShUeNGpXrySYgQkBh/fXXj4GHhOcXXHDB0KVLlzLDKHiceuqpueeyadGkhW+44YZx30inpjGl+oEGJscWwycKZZGQ4XLzzTeXK4f5FlpooXjsPvPMM1VuszrLjh49Ohx00EFhhRVWiGXn3nvvLfM62SxkXXDsUX7oVafBnJAdxXP0kNOIJgPm559/zr3+66+/xoAHQULOAYceemiZ45fzBuWV45v1UwYoP1WhQcs6CUrmByRppNPozR/WxLbTeYgME7KOkkmTJoULL7wwlmVeJzskv/yzjoMPPjh+Vso1wQG+v2J07tw5lnuCIwlBSNbDg6wtzpVgCAzb4nlkh1FwbgDfU3bIxpAhQ+IwDc7J3bt3j8ecJKk0DDZIUg3QUKC3NTuWmAosve/XXHNNHMpApZbGPCnZeOWVV8KNN94YzjnnnJjGfMIJJ4Trr78+jjHOx5ABUmq//vrr2KB46KGHYgWexk02GJCvY8eOsXGTbUiwr+PGjctlBCTMcXDGGWeEPfbYI+5D7969YzDk4osvjq//9ddfcVgCvYM0nM4+++y4v1l8NlLZGVdNAy01APv161fU90jjpFmzZnG4QQooMMyGRgn7y980ZlKPZf5nAEGU0047Lf5OcCKlN9MgooFCwIFx0jRu6BFX/fDjjz/GdPlVV1214Os0QDnW6RWuylJLLRXXx5wapVqWMkmjnbLDkI4LLrggvPjii/E1fp577rmxvBLoo6wRCCD4CMowwUOCDLyf1wk0pOOYDJ7USObYvfvuu2OKPvMUEIQAxzUZGAQkCbjNNddcMWBQDIKCnIOyGUHsJ1kdWZQhzg+UUYKIt912WyxHDDUhswRkl7BdzhsPPPBAGDlyZG5eBLC/BFUWXXTROMTohhtuiO9lvXzOYhDISOeIVM45F/D/5/yUgpLMyUDwKZuxkX0P+I6z5wE+12GHHRY/A4Fi/kdVBXUlScUx2CBJRSBIQCOWBz1gBBk6depUZrw4lV/GA6+00krxNTIGmJMgTRr2ww8/xEo7AQF6UGkk08hIqcFZZBCQPn7ZZZfFijO9e1TqyYogg6KqhkQ22EAjggkts40yGhkEBWg0MAxkscUWCzvssEPstbz//vtjoIHgAVkZNCKWXHLJ+PlSYyghEEJFnUYKaehsh95YGkjF9LLSMOA7Tb2W9FjSg8r3RAODQAMBB/aXZQoFG1hHSpem15b3prHpBD0Yv02jhDHSn376aaX7Q4Mv/Z+zj3zbbrttuWWyDSzNuHScp6EoM4KGOFIDuRTLUhbp8V988cVjYIByR2Mcbdu2jcEHyhTlncwDxuWncwHr/uOPP8ICCywQXycT6sorr8zNR0LZI0BIlgDHLwEQ1sd4f4IclAcCEGQdcSwy1IjgJsNAikE5JWiQGvDsF/NibLTRRmWWY/JWMh1SOeLc1r9//zj54hNPPBE/B4EOApW8l/MEWQ7zzTdfbh2cT5gLhUY85zHOn3xW1pGfXVFVsIHPTSYD+865gHMar3HeAOcKAkDNmzcvt440RIFzRXZ4xRFHHBEzUxZZZJF47iKoW9Eku0nK0sg+8oPG2WtGepx55plFfV5Jaiics0GSikBDNfU0knVAz9cVV1wRG+r0+tPApfeO7IXHHnsspjuTlcAQgTTnAMEHUnSp6DMUYN11142/E3jIR+WfRhYNmWxP7sknn1zlvm655ZYx24BKOanDBBsuueSSMsuQbkzqcX4PIBOtkb7OxIzsA0GIbMU82/BmHfRiXn755XFsfUJlnUADn71QpT8fjQW+t9T7mCZOo9FHkIBhETReaHzynRWLMd00zhLWxyRzlaHxRyOuKvQq8//Iyv9bMybNfUF2w4wieIbs8VDMsvTC02hMCC6SsYD8skND/LXXXou/E0AknZ9hUpQlzhcED9PwAso2WQ/nnXdebLwzLwGNdQIWIEOK4z0/EEm5Yr0EKmhwM4Qji6AGrxdTNlg3GVhkjtCrz/CRFKhLOAcQHMg+T6Od8xKvMc8D54vsflDmCbImfJavvvqqXNAufZZicI7gOOC75BzB+tPxQdCBbDIQ8MvPzqhK9hybAltVBUoJ/PL/zv9OK7pmJMUcf5LUkBhskKQiUAklDTihh47nCDAwWRnjpkmLplJNTyNZC/RWMkwhoXJMbyA9dG+99VasNN91111xrDbDECq7+0F1ELyg0UGvIWnKpGzTsMiOB69oQsXUGGL7zEqfP/Y6u1/pNXpUCwUBGGef5mKoqiFBo4zMDwI0abw1+J3vioY8vbbVmaG9mPT6fHy+7P+5su+Y7BXNPGTKEGQio6XQfCM0VOnt5/ijR70y9FQTOCtmAtbssjQYUwAgv7HIcJ8sykNqlD/11FNxWBXBCRrzrIfGeQpUgGFUnD8IUJDJROCBYREMvWJdNILzhy2BTIN0x4j8clyd8wbfKetnPwlIFhpiVNl5gsyhYvaDZQmmcFvRfMVO4Ej55/tI5878cwQZA/zfCLCSWVEd+f/HQp+n0P5UdZ7Iv2ZI0uzIYRSSVEOpQkplmgkZX3/99djDTyOCLAbScmlAp+VIsyWlOE2yRjo0wzEKjbMm84GezezYYTIJGGLA7R+LyW6g15JGBL/nV6hpxPFgjHMWPYM0Ith30qbJ0GC7SXYYAj15NP4Z306lOj2o9JMmXSx6RRkKQeCFlPJsTyO9ljQg2K/Kshrq2u36NOM4Zhl6QJo+Kf75aJgz4SLHTGXIvuGOBdkhT8Uuy3CI7LGd7b3OT7WnLKWgB5kv7DtDkMh+IthHOQHnA3roaXyzvr322itmN/B5CKBwvDNsguAgjfG0bQJcDKtiqADzNxDMyy+/VQ0TyiKTgewI5l2pKGuIzCi+4zRvCsiI4rxEwJWySiZDdvJGMr/4DAnfCZ+L/U2fhYY4QYE0rKQY7B/bIeCQHU7F/5/gEBN0cj5KE0FKkmqfwQZJKgIZAlTMedBbT+OXyjJp9/TM03CnN4/GPY0KKuiMv2b5VFEnNZfhDPRcMsSAddBwKDQnAOskfZlhE0zwSMYEv1OZLubWdPTGkuJMQ62itOIDDzwwzq3AuGwaD/TGko7MxG00cngfjSFmjqfxMHjw4NiTnG3gM3aZYQesh8AKE+MxkSTBg/yU7MoyEGiMMcQk22MJhlHwvf73v/8t91pWmkGfxlZVQyVUfzAnAg1JMgAoNxxjlIc0+SrZANm7J2TLKeXwpZdeisMVyEJhEses6ixbCPMqMEcDgQOCCxz7hx9+eHyNhjUNYwIS7DNzs1BGwPmAYAHvp0eehjhlleFXNMIZdkWwkt8JSnK3BJYhA4GAZmpMU/ZoYDOMi/cT4OO7KRbnEoKXzAux+eabF8yKIBDCRJknnnhiPAewfuZnYP85P5D9wbwvBEuYcJL9JIiSJrEE/zuGphCEZR08mBuBcyRBlereJpfzTv6koUzsyGssU1ngkWMlDUORJM18DqOQVCs6tG9br7ZJoyJN/kaPKz2eTETGxGktW7aMD3oxuX0lDQDGNXMbOe7mkOYjIIuBccdMqkhPLY0J5mygEp6PbbAcE07S8KECTSoyvZ9kHlSFNF8q5PTUMqSiEO52kWaZJ3DCJG40YAhCpIo5r9Ggo9GRGj/pNpNpHfRsEnDg8xN0YcZ8lquONG9D/gSQaQI4Us0LzTCf8N0whpp0dRpPqlyX9ovVi+1RrmikU/aY0JTefgJZjNnnmKMMVlROKSc0+hkuwHGaP4SiOssWQjl59dVX45wl9K6TdUDjHQyfIpBAQ5wyRpYQ86ikRjb7zefhPZQXhjpRTm+//fbcUA0+N+9hO7xOkJH9JaMAZEyQVcVQCLINaHCTTUHgoVgEJRmWUFFAksAL+0GZSneloIzyd5pIk2Ak5wCGiBCYYJ3ZW/gyHIZ18Fk5j1CmOTeRyVSdYVF8twQSmdsi/xxIIJLjodAEsln8b1MGSboziCRp5mk0vaqBaZJUA1QKqfSSZkvjIIsKcqFxsrNCbW5bqgumTpsaGs/ReLbZrjS7XFslqa6xxi1plqvNxr6BBs3uaqvBb6BBkqTZi7VuSZIkSZJUUgYbJEmSJElSSRlskCRJkiRJJWWwQZIkSZIklZTBBkkzlTe8kSSpNLymSqpPDDZIminSfdDHjx9f27siSVKDkK6p6RorSXVZk9reAUkNU+PGjUPbtm3DqFGj4t+tWrUKjRo1qu3dkiSpXmY0EGjgmsq1lWusJNV1jaabjyVpJuH0MnLkyPDnn3/W9q5IklTvEWho3769wXtJ9YLBBkkz3dSpU8PkyZNrezckSaq3GDphRoOk+sRggyRJkiRJKikniJQkSZIkSSVlsEGSJEmSJJWUwQZJkiRJklRSBhskSZIkSVJJGWyQJEmSJEklZbBBkiRJkiSVlMEGSZIkSZIUSun/ATP6dswwVGcsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1088x640 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load data\n",
    "df = pd.read_csv(\"model_performance_summary.csv\")\n",
    "\n",
    "# Metrics and palette\n",
    "metrics = [\"Accuracy\", \"Sensitivity\", \"Specificity\", \"F1 Score\"]\n",
    "tol_bright = [\"#88CCEE\", \"#5567AA\", \"#44AA99\", \"#117733\"]\n",
    "\n",
    "# Reshape long-form\n",
    "records = []\n",
    "for metric in metrics:\n",
    "    for _, row in df.iterrows():\n",
    "        records.append({\n",
    "            \"Metric\": metric,\n",
    "            \"Model\": row[\"Model\"],\n",
    "            \"Dataset\": row[\"Dataset\"],\n",
    "            \"Mean\": row[f\"{metric}_mean\"],\n",
    "            \"Std\": row[f\"{metric}_std\"]\n",
    "        })\n",
    "df_long = pd.DataFrame(records)\n",
    "\n",
    "# Set Seaborn style\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create the grouped barplot\n",
    "g = sns.catplot(\n",
    "    data=df_long,\n",
    "    kind=\"bar\",\n",
    "    x=\"Dataset\",\n",
    "    y=\"Mean\",\n",
    "    hue=\"Model\",\n",
    "    col=\"Metric\",\n",
    "    palette=tol_bright,\n",
    "    height=3.2,\n",
    "    aspect=1.7,\n",
    "    col_wrap=2,\n",
    "    legend=False,\n",
    "    errcolor='black',\n",
    "    errwidth=1,\n",
    ")\n",
    "\n",
    "# Add error bars and value labels manually\n",
    "for ax, metric in zip(g.axes.flat, metrics):\n",
    "    df_subset = df_long[df_long[\"Metric\"] == metric].reset_index(drop=True)\n",
    "    for i, (bar, (_, row)) in enumerate(zip(ax.patches, df_subset.iterrows())):\n",
    "        # Error bar\n",
    "        ax.errorbar(\n",
    "            x=bar.get_x() + bar.get_width() / 2,\n",
    "            y=bar.get_height(),\n",
    "            yerr=row[\"Std\"],\n",
    "            color='black',\n",
    "            capsize=3,\n",
    "            fmt='none'\n",
    "        )\n",
    "        # Value label (mean rounded to 2 digits)\n",
    "        ax.text(\n",
    "            bar.get_x() + bar.get_width() / 2,\n",
    "            bar.get_height() + 0.015,\n",
    "            f\"{row['Mean']:.2f}\",\n",
    "            ha='center',\n",
    "            va='bottom',\n",
    "            fontsize=10,\n",
    "            color='black'\n",
    "        )\n",
    "\n",
    "    ax.set_ylim(0.6, 1.0)\n",
    "    ax.set_title(metric, fontweight='bold')\n",
    "    ax.set_ylabel(\"\")\n",
    "\n",
    "# Rotate x-axis tick labels\n",
    "#for ax in g.axes.flat:\n",
    " #   ax.set_xticklabels(ax.get_xticklabels(), rotation=0)\n",
    "\n",
    "\n",
    "# Add custom legend\n",
    "handles, labels = g.axes[0].get_legend_handles_labels()\n",
    "g.fig.legend(\n",
    "    handles,\n",
    "    labels,\n",
    "    title=\"Models\",\n",
    "    loc=\"lower center\",\n",
    "    bbox_to_anchor=(0.5, -0.02),\n",
    "    ncol=2,\n",
    "    fontsize=11,\n",
    "    title_fontsize=12\n",
    ")\n",
    "\n",
    "# Adjust layout\n",
    "g.fig.subplots_adjust(top=0.9, bottom=0.15)\n",
    "g.fig.suptitle(\"Model Performance per Dataset (Mean ± SD)\", fontsize=16)\n",
    "\n",
    "plt.savefig(\"model_metrics.svg\", format='svg')        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ce29c46c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1088x960 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load data\n",
    "df = pd.read_csv(\"model_performance_summary.csv\")\n",
    "\n",
    "# Metrics to plot\n",
    "metrics = [\n",
    "    \"Accuracy\", \"Precision\", \"Sensitivity\", \"Specificity\", \n",
    "    \"F1 Score\", \"MCC\"\n",
    "]\n",
    "tol_bright = [\"#88CCEE\", \"#5567AA\", \"#44AA99\", \"#117733\"]\n",
    "\n",
    "# Reshape data to long form\n",
    "records = []\n",
    "for metric in metrics:\n",
    "    for _, row in df.iterrows():\n",
    "        records.append({\n",
    "            \"Metric\": metric,\n",
    "            \"Model\": row[\"Model\"],\n",
    "            \"Dataset\": row[\"Dataset\"],\n",
    "            \"Mean\": row.get(f\"{metric}_mean\", None),\n",
    "            \"Std\": row.get(f\"{metric}_std\", None)\n",
    "        })\n",
    "df_long = pd.DataFrame(records)\n",
    "\n",
    "# Set Seaborn style\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create the grouped barplot\n",
    "g = sns.catplot(\n",
    "    data=df_long,\n",
    "    kind=\"bar\",\n",
    "    x=\"Dataset\",\n",
    "    y=\"Mean\",\n",
    "    hue=\"Model\",\n",
    "    col=\"Metric\",\n",
    "    palette=tol_bright,\n",
    "    height=3.2,\n",
    "    aspect=1.7,\n",
    "    col_wrap=2,\n",
    "    legend=False,\n",
    "    errcolor='black',\n",
    "    errwidth=1,\n",
    ")\n",
    "\n",
    "# Add error bars and value labels manually\n",
    "for ax, metric in zip(g.axes.flat, metrics):\n",
    "    df_subset = df_long[df_long[\"Metric\"] == metric].reset_index(drop=True)\n",
    "    for bar, (_, row) in zip(ax.patches, df_subset.iterrows()):\n",
    "        # Error bar\n",
    "        ax.errorbar(\n",
    "            x=bar.get_x() + bar.get_width() / 2,\n",
    "            y=bar.get_height(),\n",
    "            yerr=row[\"Std\"],\n",
    "            color='black',\n",
    "            capsize=3,\n",
    "            fmt='none'\n",
    "        )\n",
    "        # Value label\n",
    "        ax.text(\n",
    "            bar.get_x() + bar.get_width() / 2,\n",
    "            bar.get_height() + 0.015,\n",
    "            f\"{row['Mean']:.2f}\",\n",
    "            ha='center',\n",
    "            va='bottom',\n",
    "            fontsize=10,\n",
    "            color='black'\n",
    "        )\n",
    "\n",
    "    ax.set_ylim(0.6, 1.0)\n",
    "    ax.set_title(metric, fontweight='bold')\n",
    "    ax.set_ylabel(\"\")\n",
    "\n",
    "# Add custom legend\n",
    "handles, labels = g.axes[0].get_legend_handles_labels()\n",
    "g.fig.legend(\n",
    "    handles,\n",
    "    labels,\n",
    "    title=\"Models\",\n",
    "    loc=\"lower center\",\n",
    "    bbox_to_anchor=(0.5, -0.02),\n",
    "    ncol=2,\n",
    "    fontsize=11,\n",
    "    title_fontsize=12\n",
    ")\n",
    "\n",
    "# Adjust layout\n",
    "g.fig.subplots_adjust(top=0.9, bottom=0.15)\n",
    "g.fig.suptitle(\"Model Performance per Dataset (Mean ± SD)\", fontsize=16)\n",
    "\n",
    "# Save and show plot\n",
    "plt.savefig(\"model_metrics_extended.svg\", format='svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "af7ac481",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{table}\n",
      "\\centering\n",
      "\\caption{Model performance by dataset and metric (mean ± standard deviation).}\n",
      "\\label{tab:model_performance}\n",
      "\\begin{tabular}{lcccc}\n",
      "\\toprule\n",
      "{} &        Basic Model No FFT &      Basic Model With FFT &    CDD-based Model No FFT &  CDD-based Model With FFT \\\\\n",
      "\\midrule\n",
      "Accuracy (Basic)        &  \\textbf{0.9810 ± 0.0016} &           0.9799 ± 0.0010 &           0.9773 ± 0.0024 &           0.9796 ± 0.0012 \\\\\n",
      "Accuracy (CDD-based)    &           0.9235 ± 0.0061 &           0.8942 ± 0.0224 &           0.9385 ± 0.0024 &  \\textbf{0.9562 ± 0.0027} \\\\\n",
      "Accuracy (External)     &           0.8224 ± 0.0045 &           0.8088 ± 0.0047 &           0.8663 ± 0.0047 &  \\textbf{0.8888 ± 0.0058} \\\\\n",
      "Precision (Basic)       &           0.9928 ± 0.0008 &  \\textbf{0.9964 ± 0.0012} &           0.9835 ± 0.0028 &           0.9886 ± 0.0010 \\\\\n",
      "Precision (CDD-based)   &           0.9766 ± 0.0017 &  \\textbf{0.9875 ± 0.0012} &           0.9582 ± 0.0034 &           0.9708 ± 0.0046 \\\\\n",
      "Precision (External)    &           0.9704 ± 0.0030 &  \\textbf{0.9827 ± 0.0026} &           0.9561 ± 0.0020 &           0.9627 ± 0.0035 \\\\\n",
      "Sensitivity (Basic)     &           0.9841 ± 0.0020 &           0.9792 ± 0.0016 &  \\textbf{0.9891 ± 0.0015} &           0.9866 ± 0.0022 \\\\\n",
      "Sensitivity (CDD-based) &           0.9153 ± 0.0093 &           0.8638 ± 0.0326 &           0.9560 ± 0.0039 &  \\textbf{0.9683 ± 0.0061} \\\\\n",
      "Sensitivity (External)  &           0.7891 ± 0.0080 &           0.7605 ± 0.0081 &           0.8627 ± 0.0073 &  \\textbf{0.8873 ± 0.0097} \\\\\n",
      "Specificity (Basic)     &           0.9662 ± 0.0038 &  \\textbf{0.9834 ± 0.0052} &           0.9215 ± 0.0129 &           0.9474 ± 0.0047 \\\\\n",
      "Specificity (CDD-based) &           0.9441 ± 0.0054 &  \\textbf{0.9718 ± 0.0036} &           0.8937 ± 0.0090 &           0.9252 ± 0.0118 \\\\\n",
      "Specificity (External)  &           0.9254 ± 0.0083 &  \\textbf{0.9584 ± 0.0067} &           0.8774 ± 0.0064 &           0.8934 ± 0.0113 \\\\\n",
      "F1 Score (Basic)        &  \\textbf{0.9467 ± 0.0046} &           0.9456 ± 0.0023 &           0.9341 ± 0.0071 &           0.9429 ± 0.0030 \\\\\n",
      "F1 Score (CDD-based)    &           0.8745 ± 0.0080 &           0.8383 ± 0.0263 &           0.8912 ± 0.0045 &  \\textbf{0.9222 ± 0.0041} \\\\\n",
      "F1 Score (External)     &           0.7178 ± 0.0042 &           0.7099 ± 0.0040 &           0.7621 ± 0.0060 &  \\textbf{0.7969 ± 0.0076} \\\\\n",
      "MCC (Basic)             &  \\textbf{0.9354 ± 0.0054} &           0.9344 ± 0.0028 &           0.9205 ± 0.0085 &           0.9305 ± 0.0037 \\\\\n",
      "MCC (CDD-based)         &           0.8246 ± 0.0113 &           0.7786 ± 0.0354 &           0.8483 ± 0.0060 &  \\textbf{0.8919 ± 0.0059} \\\\\n",
      "MCC (External)          &           0.6307 ± 0.0051 &           0.6267 ± 0.0042 &           0.6827 ± 0.0079 &  \\textbf{0.7298 ± 0.0098} \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\\end{table}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\marti\\AppData\\Local\\Temp\\ipykernel_5812\\1812577379.py:51: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  latex_table = latex_df.to_latex(\n"
     ]
    }
   ],
   "source": [
    "# Create a Latex table from the summary data\n",
    "\n",
    "# Load the summary data\n",
    "df = pd.read_csv(\"model_performance_summary.csv\")\n",
    "\n",
    "# Metrics and models (to control ordering)\n",
    "metrics = [\"Accuracy\", \"Precision\", \"Sensitivity\", \"Specificity\", \n",
    "    \"F1 Score\", \"MCC\"]\n",
    "datasets = [\"Basic\", \"CDD-based\", \"External\"]\n",
    "models = [\n",
    "    \"Basic Model No FFT\",\n",
    "    \"Basic Model With FFT\",\n",
    "    \"CDD-based Model No FFT\",\n",
    "    \"CDD-based Model With FFT\"\n",
    "]\n",
    "\n",
    "# Create a nested dictionary for easier formatting\n",
    "table_data = {}\n",
    "\n",
    "for metric in metrics:\n",
    "    for dataset in datasets:\n",
    "        row_label = f\"{metric} ({dataset})\"\n",
    "        table_data[row_label] = {}\n",
    "\n",
    "        # Temporarily collect values to find the best\n",
    "        values = {}\n",
    "        for model in models:\n",
    "            row = df[(df[\"Model\"] == model) & (df[\"Dataset\"] == dataset)]\n",
    "            if not row.empty:\n",
    "                mean = row[f\"{metric}_mean\"].values[0]\n",
    "                std = row[f\"{metric}_std\"].values[0]\n",
    "                values[model] = (mean, f\"{mean:.4f} ± {std:.4f}\")\n",
    "            else:\n",
    "                values[model] = (None, \"--\")\n",
    "\n",
    "        # Find the highest mean (excluding missing)\n",
    "        max_mean = max((v[0] for v in values.values() if v[0] is not None))\n",
    "\n",
    "        # Populate final table, bold the best\n",
    "        for model in models:\n",
    "            val = values[model]\n",
    "            if val[0] == max_mean:\n",
    "                table_data[row_label][model] = f\"\\\\textbf{{{val[1]}}}\"\n",
    "            else:\n",
    "                table_data[row_label][model] = val[1]\n",
    "\n",
    "# Convert to DataFrame\n",
    "latex_df = pd.DataFrame.from_dict(table_data, orient=\"index\", columns=models)\n",
    "\n",
    "# Output LaTeX\n",
    "latex_table = latex_df.to_latex(\n",
    "    index=True,\n",
    "    escape=False,  # Keep ± symbol\n",
    "    column_format=\"lcccc\",\n",
    "    multicolumn=True,\n",
    "    multicolumn_format=\"c\",\n",
    "    caption=\"Model performance by dataset and metric (mean ± standard deviation).\",\n",
    "    label=\"tab:model_performance\"\n",
    ")\n",
    "\n",
    "# Save or print\n",
    "with open(\"model_performance_table.tex\", \"w\") as f:\n",
    "    f.write(latex_table)\n",
    "\n",
    "print(latex_table)"
   ]
  }
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